How is oil analysis used for condition monitoring?
Oil analysis: How, why and when?
Oil analysis was first used in 1946, when the US railroad industry analyzed diesel engine lubricant to detect component wear and tear. Spent oil was shipped to researchers who used a spectrograph to detect individual chemical elements such as iron and copper. The technique began expanding to other industries in the late 1950s, as handheld spectrometers were developed that could analyze samples on the spot.
Oil analysis sensor technology
Oil sensors come in many different types. Some measure the oil’s dielectric constant, which changes as the oil degrades or becomes contaminated. (A substance’s dielectric constant reflects its ability to keep an electric field from forming in it.) Other oil sensors measure optical characteristics and compare them to model conditions to assess the oil’s quality (a technique called Fourier transform infrared spectroscopy). Still others use magnetic fields to detect and classify metallic particles in the oil (a sign of wear). And still others again use x-ray emissions to detect the presence of foreign elements.
Oil sensors need to be placed on or near the asset that is being monitored. For this reason, oil analysis sensors are not suited to monitoring assets that are:
- inaccessible (such as underground pumps)
- remote or widely spaced (such as offshore wind turbines)
- situated in hard-to-reach places
- situated in hazardous environments, such as ATEX zones
- situated in harsh conditions, such as hot strip steel mills where extreme temperatures can damage the sensors and the resultant flow of data
Oil analysis performance in fault detection
Below is a P-F curve demonstrating how oil analysis compares to other condition monitoring techniques when it comes to fault detection in advance of an asset breakdown. This is a P-F curve for bearing failure in a specific production system.
For more information on the accuracy of oil analysis in comparison to other condition monitoring techniques, download the condition monitoring comparison guide.
A sample P-F curve for bearing failure in a specific production system. The locations of the various technologies on the curve will be different for each piece of equipment, production environment and failure mode, so be sure to calculate it for the specific assets and types of degradation you want to monitor.
Using oil analysis for fault detection: general rules of thumb
Every production system is different, meaning there’s no one-size-fits-all condition monitoring technology. However, we can state some general rules of thumb when it comes to areas where oil analysis is strong or weak in fault detection.
Strong in monitoring:
- in noisy or vibrating environments
- one motor driving many assets
- mechanical faults
- assets driven by direct current (DC)
- rotating machinery (with the caveat that not all assets have oil that can be analyzed)
- very slowly rotating machinery
Weak (or not possible) in monitoring:
- remote or inaccessible assets
- assets located in ATEX zones or other harsh conditions
- electrical faults
- energy insights
Compare oil analysis and other condition monitoring techniques
Download the condition monitoring comparison guide for a full comparison of oil analysis and the following widely used techniques:
- Vibration analysis
- MCSA (motor current signature analysis)
- MCSA + voltage
- Acoustic emission analysis
- Infrared thermography
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Vibration sensors come in different forms, but the most widely used are piezoelectric accelerometers: devices that translate the mechanical force generated by a change in motion into a proportional electrical charge.
Optimizing maintenance stops in a hot rolling mill is easier said than done. Start with a variety of developing faults you need to correct before they fail, spread them over more assets than you have time to check, and add…
Download The condition monitoring comparison guide for hot strip mills to find out which technique is right for your mill.